{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Copyright (c) Microsoft Corporation.</i>\n",
    "\n",
    "<i>Licensed under the MIT License.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dilated Convolutional Neural Network (CNN)\n",
    "\n",
    "This notebook introduces Dilated Convolutional Neural Network (CNN) model and carries out multi-round training and evaluation of this model on the Orange Juice dataset.\n",
    "\n",
    "The Dilated CNN is built upon dilated causal convolution inspired by [WaveNet](https://arxiv.org/abs/1609.03499). [Recently study](https://arxiv.org/abs/1803.01271) shows that it outperforms canonical recurrent networks such as LSTMs over a diverse range of tasks and datasets. Dilated CNN has many advantages when handling sequential data like time series\n",
    "* Capturing long-range input information with less parameters\n",
    "* Handling temporal flow with causal connection structures\n",
    "* Better training efficiency than recurrent neural networks\n",
    "\n",
    "Dilated CNN has been applied in several machine learning competitions and achieved impressive performance, e.g. [the Favorita Grocery Sales Forecasting competition](https://github.com/LenzDu/Kaggle-Competition-Favorita)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Settings and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System version: 3.6.10 |Anaconda, Inc.| (default, Mar 23 2020, 23:13:11) \n",
      "[GCC 7.3.0]\n",
      "TensorFlow version: 2.0.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import math\n",
    "import shutil\n",
    "import random\n",
    "import datetime\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import scrapbook as sb\n",
    "\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from fclib.common.utils import git_repo_path, module_path\n",
    "from fclib.dataset.ojdata import download_ojdata, split_train_test, FIRST_WEEK_START\n",
    "from fclib.feature_engineering.feature_utils import (\n",
    "    week_of_month,\n",
    "    df_from_cartesian_product,\n",
    "    gen_sequence_array,\n",
    "    static_feature_array,\n",
    "    normalize_columns,\n",
    ")\n",
    "from fclib.models.dilated_cnn import create_dcnn_model\n",
    "from fclib.evaluation.evaluation_utils import MAPE\n",
    "from fclib.common.plot import plot_predictions_with_history\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "print(\"System version: {}\".format(sys.version))\n",
    "print(\"TensorFlow version: {}\".format(tf.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Settings\n",
    "\n",
    "In the following cell, we define global settings related to the model and feature engineering. We initialize several key parameters of the model including `SEQ_LEN` and `DROPOUT_RATE` that decide the deep learning network structure as well as `BATCH_SIZE`, `LEARNING_RATE`, and `EPOCHS` that control the optimization algorithm. We use historical data of a number of dynamic features and several static features to form input sequences to the model. The dynamic features include `deal`, `feat`, `month`, `week_of_month`, `price`, `price_ratio`; while the static features are `store` and `brand`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Use False if you've already downloaded and split the data\n",
    "DOWNLOAD_SPLIT_DATA = True\n",
    "\n",
    "# Data directories\n",
    "DATA_DIR = os.path.join(git_repo_path(), \"ojdata\")\n",
    "TRAIN_DIR = os.path.join(DATA_DIR, \"train\")\n",
    "TEST_DIR = os.path.join(DATA_DIR, \"test\")\n",
    "\n",
    "# Forecasting settings\n",
    "N_SPLITS = 10\n",
    "HORIZON = 2\n",
    "GAP = 2\n",
    "FIRST_WEEK = 40\n",
    "LAST_WEEK = 156\n",
    "\n",
    "# Parameters of the model\n",
    "SEQ_LEN = 15\n",
    "DROPOUT_RATE = 0.01\n",
    "BATCH_SIZE = 64\n",
    "LEARNING_RATE = 0.015\n",
    "EPOCHS = 25\n",
    "\n",
    "# Feature columns\n",
    "DYNAMIC_FEATURES = [\"deal\", \"feat\", \"month\", \"week_of_month\", \"price\", \"price_ratio\"]\n",
    "STATIC_FEATURES = [\"store\", \"brand\"]\n",
    "\n",
    "# Maximum store ID and brand ID\n",
    "MAX_STORE_ID = 137\n",
    "MAX_BRAND_ID = 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also fix random seeds so that the results obtained by Dilated CNN can be reproduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix random seeds\n",
    "random.seed(2)\n",
    "np.random.seed(2)\n",
    "tf.random.set_seed(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "We need to download the Orange Juice data and split it into training and test sets for multiple forecast rounds. By default, the following cell will download and spit the data. If you've already done so, you may skip this part by changing `DOWNLOAD_SPLIT_DATA` to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already exists at the specified location.\n",
      "Finished data downloading and splitting.\n"
     ]
    }
   ],
   "source": [
    "if DOWNLOAD_SPLIT_DATA:\n",
    "    download_ojdata(DATA_DIR)\n",
    "    split_train_test(\n",
    "        DATA_DIR,\n",
    "        n_splits=N_SPLITS,\n",
    "        horizon=HORIZON,\n",
    "        gap=GAP,\n",
    "        first_week=FIRST_WEEK,\n",
    "        last_week=LAST_WEEK,\n",
    "        write_csv=True,\n",
    "    )\n",
    "    print(\"Finished data downloading and splitting.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "Next we create a function to extract a number of features from the data for training the forecasting model. These features can be divided into dynamic features and static features:\n",
    "\n",
    "* Dynamic features:\n",
    "    - datetime features including week of the month, month, etc.\n",
    "    - historical weekly sales of each orange juice in recent weeks\n",
    "    - absolute price and the ratio between absolute price and average price of all brands\n",
    "    - promotion information captured by `deal` and `feat` columns\n",
    "* Static features:\n",
    "    - store ID and brand ID\n",
    "\n",
    "Note that the logarithm of the unit sales is stored in a column named `logmove` both for `train_df` and `test_df`. We compute the unit sales `move` based on this quantity and treat the unit sales as the prediction target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(pred_round, train_dir, pred_steps, offset):\n",
    "    \"\"\"Create a dataframe of the input features.\n",
    "    \n",
    "    Args: \n",
    "        pred_round (int): Prediction round (1, 2, ...)\n",
    "        train_dir (str): Path of the training data directory\n",
    "        pred_steps (int): Number of prediction steps\n",
    "        offset (int): Length of training data skipped in retraining\n",
    "\n",
    "    Returns:\n",
    "        pd.Dataframe: Dataframe including the input features in original scale\n",
    "        pd.Dataframe: Dataframe including the normalized features \n",
    "        int: Last week of the training data \n",
    "    \"\"\"\n",
    "    # Load training data\n",
    "    train_df = pd.read_csv(os.path.join(TRAIN_DIR, \"train_\" + str(pred_round) + \".csv\"))\n",
    "    train_df[\"move\"] = train_df[\"logmove\"].apply(lambda x: round(math.exp(x)))\n",
    "    train_df = train_df[[\"store\", \"brand\", \"week\", \"move\"]]\n",
    "\n",
    "    # Create a dataframe to hold all necessary data\n",
    "    store_list = train_df[\"store\"].unique()\n",
    "    brand_list = train_df[\"brand\"].unique()\n",
    "    train_end_week = train_df[\"week\"].max()\n",
    "    week_list = range(FIRST_WEEK + offset, train_end_week + GAP + HORIZON)\n",
    "    d = {\"store\": store_list, \"brand\": brand_list, \"week\": week_list}\n",
    "    data_grid = df_from_cartesian_product(d)\n",
    "    data_filled = pd.merge(data_grid, train_df, how=\"left\", on=[\"store\", \"brand\", \"week\"])\n",
    "\n",
    "    # Get future price, deal, and advertisement info\n",
    "    aux_df = pd.read_csv(os.path.join(TRAIN_DIR, \"auxi_\" + str(pred_round) + \".csv\"))\n",
    "    data_filled = pd.merge(data_filled, aux_df, how=\"left\", on=[\"store\", \"brand\", \"week\"])\n",
    "\n",
    "    # Create relative price feature\n",
    "    price_cols = [\n",
    "        \"price1\",\n",
    "        \"price2\",\n",
    "        \"price3\",\n",
    "        \"price4\",\n",
    "        \"price5\",\n",
    "        \"price6\",\n",
    "        \"price7\",\n",
    "        \"price8\",\n",
    "        \"price9\",\n",
    "        \"price10\",\n",
    "        \"price11\",\n",
    "    ]\n",
    "    data_filled[\"price\"] = data_filled.apply(lambda x: x.loc[\"price\" + str(int(x.loc[\"brand\"]))], axis=1)\n",
    "    data_filled[\"avg_price\"] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))\n",
    "    data_filled[\"price_ratio\"] = data_filled[\"price\"] / data_filled[\"avg_price\"]\n",
    "    data_filled.drop(price_cols, axis=1, inplace=True)\n",
    "\n",
    "    # Fill missing values\n",
    "    data_filled = data_filled.groupby([\"store\", \"brand\"]).apply(\n",
    "        lambda x: x.fillna(method=\"ffill\").fillna(method=\"bfill\")\n",
    "    )\n",
    "\n",
    "    # Create datetime features\n",
    "    data_filled[\"week_start\"] = data_filled[\"week\"].apply(\n",
    "        lambda x: FIRST_WEEK_START + datetime.timedelta(days=(x - 1) * 7)\n",
    "    )\n",
    "    data_filled[\"month\"] = data_filled[\"week_start\"].apply(lambda x: x.month)\n",
    "    data_filled[\"week_of_month\"] = data_filled[\"week_start\"].apply(lambda x: week_of_month(x))\n",
    "    data_filled[\"day\"] = data_filled[\"week_start\"].apply(lambda x: x.day)\n",
    "    data_filled.drop(\"week_start\", axis=1, inplace=True)\n",
    "\n",
    "    # Normalize the dataframe of features\n",
    "    cols_normalize = data_filled.columns.difference([\"store\", \"brand\", \"week\"])\n",
    "    data_scaled, min_max_scaler = normalize_columns(data_filled, cols_normalize)\n",
    "\n",
    "    return data_filled, data_scaled, train_end_week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "We then perform a multi-round training by fitting a Dilated CNN model using the training data in each forecast round. To speed up the training process, we only train the model on full training set in the first round. In the subsequent rounds, the model will be retrained using the latest data. After the model is trained in each round, it is applied to generate forecasts for the target weeks.\n",
    "\n",
    "We use a utility function `create_dcnn_model()` to define the Dilated CNN. As introduced in [literature](https://arxiv.org/abs/1609.03499), the core structure of the Dilated CNN is a stack of dilated causal convolutional layers. Below is an example of such stack involving 4 dilated convolutional layers.\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/20047467/75483041-f6814700-5973-11ea-8e76-91ce39aed8ba.png\" width=\"720\" height=\"450\">\n",
    "\n",
    "The number of dilated layers can be specified via input argument `n_dilated_layers` of the model definition utility function. After creating the model, we can print out the structure of the network via executing `model.summary()`.\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/20047467/75485586-f6377a80-5978-11ea-9a6f-547005a6a10e.png\" width=\"720\" height=\"1000\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Input and Output\n",
    "\n",
    "Next, we define two functions that help prepare input and output data for model training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_training_io(data_filled, data_scaled, train_end_week):\n",
    "    \"\"\"Prepare input and output for model training.\n",
    "    \n",
    "    Args: \n",
    "        data_filled (pd.Dataframe): Dataframe including the input features in original scale\n",
    "        data_scaled (pd.Dataframe): Dataframe including the normalized features \n",
    "        train_end_week (int): Last week of the training data\n",
    "\n",
    "    Returns:\n",
    "        np.array: Input sequences of dynamic features\n",
    "        np.array: Input sequences of categorical features\n",
    "        np.array: Output sequences of the target variable\n",
    "    \"\"\"\n",
    "    # Create sequence array for 'move'\n",
    "    start_timestep = 0\n",
    "    end_timestep = train_end_week - FIRST_WEEK - HORIZON - GAP + 1\n",
    "    train_input1 = gen_sequence_array(\n",
    "        data_scaled, SEQ_LEN, [\"move\"], \"store\", \"brand\", start_timestep, end_timestep - offset,\n",
    "    )\n",
    "\n",
    "    # Create sequence array for other dynamic features\n",
    "    start_timestep = HORIZON + GAP - 1\n",
    "    end_timestep = train_end_week - FIRST_WEEK\n",
    "    train_input2 = gen_sequence_array(\n",
    "        data_scaled, SEQ_LEN, DYNAMIC_FEATURES, \"store\", \"brand\", start_timestep, end_timestep - offset,\n",
    "    )\n",
    "\n",
    "    seq_in = np.concatenate((train_input1, train_input2), axis=2)\n",
    "\n",
    "    # Create array of static features\n",
    "    total_timesteps = train_end_week - FIRST_WEEK - SEQ_LEN - HORIZON - GAP + 3\n",
    "    cat_fea_in = static_feature_array(data_filled, total_timesteps - offset, STATIC_FEATURES, \"store\", \"brand\")\n",
    "\n",
    "    # Create training output\n",
    "    start_timestep = SEQ_LEN + GAP - 1\n",
    "    end_timestep = train_end_week - FIRST_WEEK\n",
    "    train_output = gen_sequence_array(\n",
    "        data_filled, HORIZON, [\"move\"], \"store\", \"brand\", start_timestep, end_timestep - offset,\n",
    "    )\n",
    "    train_output = np.squeeze(train_output)\n",
    "\n",
    "    return seq_in, cat_fea_in, train_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_testing_io(data_filled, data_scaled, train_end_week):\n",
    "    \"\"\"Prepare input and output for model training.\n",
    "    \n",
    "    Args: \n",
    "        data_filled (pd.Dataframe): Dataframe including the input features in original scale\n",
    "        data_scaled (pd.Dataframe): Dataframe including the normalized features \n",
    "        train_end_week (int): Last week of the training data\n",
    "\n",
    "    Returns:\n",
    "        np.array: Input sequences of dynamic features\n",
    "        np.array: Input sequences of categorical features\n",
    "    \"\"\"\n",
    "    # Get inputs for prediction\n",
    "    start_timestep = train_end_week - FIRST_WEEK - SEQ_LEN + 1\n",
    "    end_timestep = train_end_week - FIRST_WEEK\n",
    "    test_input1 = gen_sequence_array(\n",
    "        data_scaled, SEQ_LEN, [\"move\"], \"store\", \"brand\", start_timestep - offset, end_timestep - offset,\n",
    "    )\n",
    "\n",
    "    start_timestep = train_end_week + GAP + HORIZON - FIRST_WEEK - SEQ_LEN\n",
    "    end_timestep = train_end_week + GAP + HORIZON - FIRST_WEEK - 1\n",
    "    test_input2 = gen_sequence_array(\n",
    "        data_scaled, SEQ_LEN, DYNAMIC_FEATURES, \"store\", \"brand\", start_timestep - offset, end_timestep - offset,\n",
    "    )\n",
    "\n",
    "    seq_in = np.concatenate((test_input1, test_input2), axis=2)\n",
    "\n",
    "    total_timesteps = 1\n",
    "    cat_fea_in = static_feature_array(data_filled, total_timesteps, STATIC_FEATURES, \"store\", \"brand\")\n",
    "\n",
    "    return seq_in, cat_fea_in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training and Prediction\n",
    "\n",
    "With the above helper functions, now we are ready for model training and applying the trained model to generate predictions. We save the training log information in the directory `log_dir` so that we can visualize the training process with [TensorBoard](https://www.tensorflow.org/tensorboard/get_started), which is a tool for providing the measurements and visualizations needed during the machine learning workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Round 1 ----\n",
      "Train on 72127 samples\n",
      "Epoch 1/25\n",
      "71616/72127 [============================>.] - ETA: 0s - loss: 56.5520 - mape: 56.5520 - mae: 7724.2822\n",
      "Epoch 00001: loss improved from inf to 56.51509, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 6s 84us/sample - loss: 56.5151 - mape: 56.5151 - mae: 7720.3770\n",
      "Epoch 2/25\n",
      "71808/72127 [============================>.] - ETA: 0s - loss: 47.1695 - mape: 47.1694 - mae: 7017.5864\n",
      "Epoch 00002: loss improved from 56.51509 to 47.16605, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 47.1660 - mape: 47.1660 - mae: 7016.5732\n",
      "Epoch 3/25\n",
      "71808/72127 [============================>.] - ETA: 0s - loss: 45.7213 - mape: 45.7213 - mae: 6850.9878\n",
      "Epoch 00003: loss improved from 47.16605 to 45.71347, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 45.7135 - mape: 45.7135 - mae: 6850.3550\n",
      "Epoch 4/25\n",
      "71424/72127 [============================>.] - ETA: 0s - loss: 44.6667 - mape: 44.6667 - mae: 6717.0830\n",
      "Epoch 00004: loss improved from 45.71347 to 44.66833, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 44.6683 - mape: 44.6683 - mae: 6713.0122\n",
      "Epoch 5/25\n",
      "71616/72127 [============================>.] - ETA: 0s - loss: 43.2272 - mape: 43.2272 - mae: 6515.4497\n",
      "Epoch 00005: loss improved from 44.66833 to 43.20723, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 43.2072 - mape: 43.2072 - mae: 6508.6270\n",
      "Epoch 6/25\n",
      "71552/72127 [============================>.] - ETA: 0s - loss: 41.1341 - mape: 41.1341 - mae: 6276.3589\n",
      "Epoch 00006: loss improved from 43.20723 to 41.13340, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 41.1334 - mape: 41.1334 - mae: 6273.2939\n",
      "Epoch 7/25\n",
      "71744/72127 [============================>.] - ETA: 0s - loss: 39.7776 - mape: 39.7776 - mae: 6087.9951\n",
      "Epoch 00007: loss improved from 41.13340 to 39.76846, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 39.7685 - mape: 39.7685 - mae: 6086.8159\n",
      "Epoch 8/25\n",
      "72064/72127 [============================>.] - ETA: 0s - loss: 38.4566 - mape: 38.4566 - mae: 5858.4277\n",
      "Epoch 00008: loss improved from 39.76846 to 38.45820, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 38.4582 - mape: 38.4582 - mae: 5857.4014\n",
      "Epoch 9/25\n",
      "71808/72127 [============================>.] - ETA: 0s - loss: 37.5734 - mape: 37.5734 - mae: 5705.5396\n",
      "Epoch 00009: loss improved from 38.45820 to 37.58665, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 37.5866 - mape: 37.5867 - mae: 5701.1484\n",
      "Epoch 10/25\n",
      "72064/72127 [============================>.] - ETA: 0s - loss: 37.2489 - mape: 37.2489 - mae: 5598.1396\n",
      "Epoch 00010: loss improved from 37.58665 to 37.24802, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 37.2480 - mape: 37.2480 - mae: 5596.7349\n",
      "Epoch 11/25\n",
      "71680/72127 [============================>.] - ETA: 0s - loss: 36.7482 - mape: 36.7482 - mae: 5497.1836\n",
      "Epoch 00011: loss improved from 37.24802 to 36.74330, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 36.7433 - mape: 36.7433 - mae: 5500.9717\n",
      "Epoch 12/25\n",
      "71360/72127 [============================>.] - ETA: 0s - loss: 36.4275 - mape: 36.4275 - mae: 5431.5015\n",
      "Epoch 00012: loss improved from 36.74330 to 36.41299, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 36.4130 - mape: 36.4130 - mae: 5432.9189\n",
      "Epoch 13/25\n",
      "71104/72127 [============================>.] - ETA: 0s - loss: 36.1517 - mape: 36.1517 - mae: 5387.0752\n",
      "Epoch 00013: loss improved from 36.41299 to 36.15361, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 36.1536 - mape: 36.1536 - mae: 5391.8271\n",
      "Epoch 14/25\n",
      "71808/72127 [============================>.] - ETA: 0s - loss: 35.9469 - mape: 35.9469 - mae: 5346.2378\n",
      "Epoch 00014: loss improved from 36.15361 to 35.94919, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 35.9492 - mape: 35.9492 - mae: 5343.6650\n",
      "Epoch 15/25\n",
      "71424/72127 [============================>.] - ETA: 0s - loss: 35.8748 - mape: 35.8748 - mae: 5292.9468\n",
      "Epoch 00015: loss improved from 35.94919 to 35.88297, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 35.8830 - mape: 35.8830 - mae: 5301.1138\n",
      "Epoch 16/25\n",
      "72000/72127 [============================>.] - ETA: 0s - loss: 35.7022 - mape: 35.7022 - mae: 5275.9946\n",
      "Epoch 00016: loss improved from 35.88297 to 35.69789, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 35.6979 - mape: 35.6979 - mae: 5275.7202\n",
      "Epoch 17/25\n",
      "71040/72127 [============================>.] - ETA: 0s - loss: 35.5128 - mape: 35.5128 - mae: 5253.6084\n",
      "Epoch 00017: loss improved from 35.69789 to 35.48755, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 35.4876 - mape: 35.4876 - mae: 5249.2109\n",
      "Epoch 18/25\n",
      "71424/72127 [============================>.] - ETA: 0s - loss: 35.4470 - mape: 35.4470 - mae: 5227.4438\n",
      "Epoch 00018: loss improved from 35.48755 to 35.42664, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 35.4266 - mape: 35.4267 - mae: 5216.9648\n",
      "Epoch 19/25\n",
      "71488/72127 [============================>.] - ETA: 0s - loss: 35.3327 - mape: 35.3327 - mae: 5182.2876\n",
      "Epoch 00019: loss improved from 35.42664 to 35.33811, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 35.3381 - mape: 35.3381 - mae: 5188.2520\n",
      "Epoch 20/25\n",
      "71104/72127 [============================>.] - ETA: 0s - loss: 35.5015 - mape: 35.5016 - mae: 5198.1392\n",
      "Epoch 00020: loss did not improve from 35.33811\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 35.4936 - mape: 35.4936 - mae: 5191.4199\n",
      "Epoch 21/25\n",
      "71872/72127 [============================>.] - ETA: 0s - loss: 35.1813 - mape: 35.1813 - mae: 5150.4614\n",
      "Epoch 00021: loss improved from 35.33811 to 35.18649, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 35.1865 - mape: 35.1865 - mae: 5162.1743\n",
      "Epoch 22/25\n",
      "71744/72127 [============================>.] - ETA: 0s - loss: 35.1639 - mape: 35.1639 - mae: 5124.0410\n",
      "Epoch 00022: loss improved from 35.18649 to 35.16992, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 35.1699 - mape: 35.1699 - mae: 5127.2227\n",
      "Epoch 23/25\n",
      "72000/72127 [============================>.] - ETA: 0s - loss: 35.0720 - mape: 35.0720 - mae: 5120.6782\n",
      "Epoch 00023: loss improved from 35.16992 to 35.06942, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 35.0694 - mape: 35.0694 - mae: 5118.3569\n",
      "Epoch 24/25\n",
      "71680/72127 [============================>.] - ETA: 0s - loss: 35.0583 - mape: 35.0583 - mae: 5102.2280\n",
      "Epoch 00024: loss improved from 35.06942 to 35.06686, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 50us/sample - loss: 35.0669 - mape: 35.0669 - mae: 5103.9692\n",
      "Epoch 25/25\n",
      "71488/72127 [============================>.] - ETA: 0s - loss: 34.9209 - mape: 34.9209 - mae: 5069.0962\n",
      "Epoch 00025: loss improved from 35.06686 to 34.92286, saving model to dcnn_model.h5\n",
      "72127/72127 [==============================] - 4s 49us/sample - loss: 34.9229 - mape: 34.9228 - mae: 5068.9219\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   137      1      9910.0\n",
      "1      2      1   138      1     12029.0\n",
      "2      2      2   137      1      7981.0\n",
      "3      2      2   138      1     28129.0\n",
      "4      2      3   137      1      1061.0\n",
      "\n",
      "---- Round 2 ----\n",
      "Train on 35607 samples\n",
      "35264/35607 [============================>.] - ETA: 0s - loss: 33.9054 - mape: 33.9054 - mae: 5311.6572\n",
      "Epoch 00001: loss improved from inf to 33.92596, saving model to dcnn_model.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35607/35607 [==============================] - 2s 70us/sample - loss: 33.9260 - mape: 33.9260 - mae: 5309.5166\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   139      2      3998.0\n",
      "1      2      1   140      2      4596.0\n",
      "2      2      2   139      2      6158.0\n",
      "3      2      2   140      2      6448.0\n",
      "4      2      3   139      2      1554.0\n",
      "\n",
      "---- Round 3 ----\n",
      "Train on 35607 samples\n",
      "34752/35607 [============================>.] - ETA: 0s - loss: 33.5213 - mape: 33.5213 - mae: 5126.2217\n",
      "Epoch 00001: loss improved from inf to 33.46539, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 3s 72us/sample - loss: 33.4654 - mape: 33.4654 - mae: 5107.3125\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   141      3      6915.0\n",
      "1      2      1   142      3      6761.0\n",
      "2      2      2   141      3      5557.0\n",
      "3      2      2   142      3      6409.0\n",
      "4      2      3   141      3      1428.0\n",
      "\n",
      "---- Round 4 ----\n",
      "Train on 35607 samples\n",
      "35200/35607 [============================>.] - ETA: 0s - loss: 33.2054 - mape: 33.2054 - mae: 5057.2705\n",
      "Epoch 00001: loss improved from inf to 33.20649, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 2s 65us/sample - loss: 33.2065 - mape: 33.2065 - mae: 5055.4448\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   143      4     18794.0\n",
      "1      2      1   144      4     17115.0\n",
      "2      2      2   143      4      5900.0\n",
      "3      2      2   144      4      5871.0\n",
      "4      2      3   143      4      1976.0\n",
      "\n",
      "---- Round 5 ----\n",
      "Train on 35607 samples\n",
      "35072/35607 [============================>.] - ETA: 0s - loss: 33.4395 - mape: 33.4395 - mae: 5141.1123\n",
      "Epoch 00001: loss improved from inf to 33.43004, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 2s 66us/sample - loss: 33.4300 - mape: 33.4300 - mae: 5135.1890\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   145      5     23389.0\n",
      "1      2      1   146      5      9354.0\n",
      "2      2      2   145      5      5825.0\n",
      "3      2      2   146      5      7065.0\n",
      "4      2      3   145      5      1998.0\n",
      "\n",
      "---- Round 6 ----\n",
      "Train on 35607 samples\n",
      "34816/35607 [============================>.] - ETA: 0s - loss: 33.1314 - mape: 33.1314 - mae: 5129.1152\n",
      "Epoch 00001: loss improved from inf to 33.11792, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 2s 66us/sample - loss: 33.1179 - mape: 33.1179 - mae: 5124.7051\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   147      6     13091.0\n",
      "1      2      1   148      6      7817.0\n",
      "2      2      2   147      6      5191.0\n",
      "3      2      2   148      6      6077.0\n",
      "4      2      3   147      6      2291.0\n",
      "\n",
      "---- Round 7 ----\n",
      "Train on 35607 samples\n",
      "35008/35607 [============================>.] - ETA: 0s - loss: 33.1471 - mape: 33.1471 - mae: 5115.1094\n",
      "Epoch 00001: loss improved from inf to 33.11932, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 2s 66us/sample - loss: 33.1193 - mape: 33.1193 - mae: 5113.9355\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   149      7      6490.0\n",
      "1      2      1   150      7      7105.0\n",
      "2      2      2   149      7      6028.0\n",
      "3      2      2   150      7     18761.0\n",
      "4      2      3   149      7      2231.0\n",
      "\n",
      "---- Round 8 ----\n",
      "Train on 35607 samples\n",
      "35392/35607 [============================>.] - ETA: 0s - loss: 33.1963 - mape: 33.1963 - mae: 4968.2534\n",
      "Epoch 00001: loss improved from inf to 33.17502, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 2s 67us/sample - loss: 33.1750 - mape: 33.1750 - mae: 4969.2456\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   151      8      8015.0\n",
      "1      2      1   152      8      7308.0\n",
      "2      2      2   151      8      6558.0\n",
      "3      2      2   152      8      8073.0\n",
      "4      2      3   151      8      2495.0\n",
      "\n",
      "---- Round 9 ----\n",
      "Train on 35607 samples\n",
      "34880/35607 [============================>.] - ETA: 0s - loss: 33.0961 - mape: 33.0961 - mae: 4771.2114\n",
      "Epoch 00001: loss improved from inf to 33.10128, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 2s 67us/sample - loss: 33.1013 - mape: 33.1013 - mae: 4765.5112\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   153      9      6429.0\n",
      "1      2      1   154      9     10321.0\n",
      "2      2      2   153      9      6320.0\n",
      "3      2      2   154      9      6201.0\n",
      "4      2      3   153      9      2790.0\n",
      "\n",
      "---- Round 10 ----\n",
      "Train on 35607 samples\n",
      "35392/35607 [============================>.] - ETA: 0s - loss: 33.3706 - mape: 33.3706 - mae: 4863.7505\n",
      "Epoch 00001: loss improved from inf to 33.35374, saving model to dcnn_model.h5\n",
      "35607/35607 [==============================] - 2s 65us/sample - loss: 33.3537 - mape: 33.3537 - mae: 4861.4824\n",
      "\n",
      " Prediction results:\n",
      "   store  brand  week  round  prediction\n",
      "0      2      1   155     10      5501.0\n",
      "1      2      1   156     10      9760.0\n",
      "2      2      2   155     10      4968.0\n",
      "3      2      2   156     10      6416.0\n",
      "4      2      3   155     10      2555.0\n",
      "\n",
      "CPU times: user 6min 43s, sys: 25.5 s, total: 7min 9s\n",
      "Wall time: 4min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Model file name and log directory\n",
    "model_file_name = \"dcnn_model.h5\"\n",
    "log_dir = os.path.join(\"logs\", \"scalars\")\n",
    "\n",
    "# Remove log directory if it exists\n",
    "if os.path.isdir(log_dir):\n",
    "    shutil.rmtree(log_dir)\n",
    "    print(\"Removed existing log directory {} \\n\".format(log_dir))\n",
    "\n",
    "# Train models and make predictions\n",
    "pred_all = []\n",
    "for r in range(1, N_SPLITS + 1):\n",
    "    print(\"---- Round \" + str(r) + \" ----\")\n",
    "    # Use offset to remove older data during retraining\n",
    "    offset = 0 if r == 1 else 40 + (r - 1) * HORIZON\n",
    "    # Create features\n",
    "    data_filled, data_scaled, train_end_week = create_features(r, TRAIN_DIR, HORIZON, offset)\n",
    "\n",
    "    # Prepare input and output for model training\n",
    "    seq_in, cat_fea_in, train_output = prepare_training_io(data_filled, data_scaled, train_end_week)\n",
    "\n",
    "    # Create and train model\n",
    "    if r == 1:\n",
    "        model = create_dcnn_model(\n",
    "            seq_len=SEQ_LEN,\n",
    "            n_dyn_fea=1 + len(DYNAMIC_FEATURES),\n",
    "            n_outputs=HORIZON,\n",
    "            n_dilated_layers=3,\n",
    "            kernel_size=2,\n",
    "            n_filters=3,\n",
    "            dropout_rate=DROPOUT_RATE,\n",
    "            max_cat_id=[MAX_STORE_ID, MAX_BRAND_ID],\n",
    "        )\n",
    "        adam = optimizers.Adam(lr=LEARNING_RATE)\n",
    "        model.compile(loss=\"mape\", optimizer=adam, metrics=[\"mape\", \"mae\"])\n",
    "        # Define checkpoint and fit model\n",
    "        checkpoint = ModelCheckpoint(model_file_name, monitor=\"loss\", save_best_only=True, mode=\"min\", verbose=1)\n",
    "        tensorboard_callback = tf.keras.callbacks.TensorBoard(\n",
    "            log_dir=os.path.join(log_dir, datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")), histogram_freq=1,\n",
    "        )\n",
    "        callbacks_list = [checkpoint, tensorboard_callback]\n",
    "        history = model.fit(\n",
    "            [seq_in, cat_fea_in],\n",
    "            train_output,\n",
    "            epochs=EPOCHS,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            callbacks=callbacks_list,\n",
    "            verbose=1,\n",
    "        )\n",
    "    else:\n",
    "        model = load_model(model_file_name)\n",
    "        checkpoint = ModelCheckpoint(model_file_name, monitor=\"loss\", save_best_only=True, mode=\"min\", verbose=1)\n",
    "        callbacks_list = [checkpoint]\n",
    "        history = model.fit(\n",
    "            [seq_in, cat_fea_in], train_output, epochs=1, batch_size=BATCH_SIZE, callbacks=callbacks_list, verbose=1,\n",
    "        )\n",
    "\n",
    "    # Prepare input for model testing\n",
    "    seq_in, cat_fea_in = prepare_testing_io(data_filled, data_scaled, train_end_week)\n",
    "\n",
    "    # Make prediction\n",
    "    pred = np.round(model.predict([seq_in, cat_fea_in]))\n",
    "\n",
    "    # Create dataframe for submission\n",
    "    exp_output = data_filled[data_filled.week >= train_end_week + GAP].reset_index(drop=True)\n",
    "    exp_output = exp_output[[\"store\", \"brand\", \"week\"]]\n",
    "    pred_df = (\n",
    "        exp_output.sort_values([\"store\", \"brand\", \"week\"]).loc[:, [\"store\", \"brand\", \"week\"]].reset_index(drop=True)\n",
    "    )\n",
    "    pred_df[\"round\"] = r\n",
    "    pred_df[\"prediction\"] = np.reshape(pred, (pred.size, 1))\n",
    "    pred_all.append(pred_df)\n",
    "\n",
    "    # Show the current predictions\n",
    "    print(\"\\n Prediction results:\")\n",
    "    print(pred_df.head(5))\n",
    "    print(\"\")\n",
    "\n",
    "pred_all = pd.concat(pred_all, axis=0)\n",
    "pred_all.rename(columns={\"move\": \"prediction\"}, inplace=True)\n",
    "pred_all = pred_all[[\"round\", \"week\", \"store\", \"brand\", \"prediction\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorBoard Monitor (Optional)\n",
    "\n",
    "We can monitor the model training process from TensorBoard. In the cell below, please switch `CHECK_TENSORBOARD` to True if you want to do so. Note that the following cell will try to find the path of the TensorBoard binary if it is not specified. In case the path can't be found, you can run `which tensorboard` (for Linux) or `where tensorboard` (for Windows) from a terminal where `forecasting_env` is activated to look for the path and replace the TensorBoard path in the second line of the code with the path that you find. \n",
    "\n",
    "To view the TensorBoard, you will need to forward port 6008 to your local machine via `ssh <user-name>@<remote-vm-ip-address> -L 6008:localhost:6008` if you're running this notebook in a remote VW. On the Tensorboard, you will see a dashboard similar to the following one:\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/20047467/75494844-24be5100-598b-11ea-97ea-96b32373e75d.png\" width=\"720\" height=\"800\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHECK_TENSORBOARD = False\n",
    "tensorboard_path = \"\" # Replace this with the path you find from terminal\n",
    "if CHECK_TENSORBOARD:\n",
    "    if not tensorboard_path:\n",
    "        # Try to find path of the TensorBoard binary\n",
    "        tensorboard_path = module_path(\"forecasting_env\", \"tensorboard\")\n",
    "    if tensorboard_path:\n",
    "        os.environ[\"TENSORBOARD_BINARY\"] = tensorboard_path\n",
    "        # Display TensorBoard\n",
    "        %tensorboard --logdir logs/scalars --port 6008\n",
    "    else:\n",
    "        print(\"Can't find TensorBoard binary. TensorBoard visualization is skipped.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "To evaluate the performance of the model, we compute MAPE of the forecasts from all the forecast rounds below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/scrapbook.scrap.json+json": {
       "data": 37.66172255231067,
       "encoder": "json",
       "name": "MAPE",
       "version": 1
      }
     },
     "metadata": {
      "scrapbook": {
       "data": true,
       "display": false,
       "name": "MAPE"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE of the predictions is 37.66172255231067\n"
     ]
    }
   ],
   "source": [
    "# Evaluate prediction accuracy\n",
    "test_all = []\n",
    "test_dir = os.path.join(DATA_DIR, \"test\")\n",
    "for r in range(1, N_SPLITS + 1):\n",
    "    test_df = pd.read_csv(os.path.join(test_dir, \"test_\" + str(r) + \".csv\"))\n",
    "    test_all.append(test_df)\n",
    "test_all = pd.concat(test_all, axis=0).reset_index(drop=True)\n",
    "test_all[\"actual\"] = test_all[\"logmove\"].apply(lambda x: round(math.exp(x)))\n",
    "test_all.drop(\"logmove\", axis=1, inplace=True)\n",
    "combined = pd.merge(pred_all, test_all, on=[\"store\", \"brand\", \"week\"], how=\"left\")\n",
    "metric_value = MAPE(combined[\"prediction\"], combined[\"actual\"]) * 100\n",
    "sb.glue(\"MAPE\", metric_value)\n",
    "print(\"MAPE of the predictions is {}\".format(metric_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Visualization\n",
    "\n",
    "Let's visually check the forecasts by plotting out the forecast results of a few sample store-brand combinations. Note that there could be gaps in the curve of actual sales due to missing sales data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = combined[[\"week\", \"store\", \"brand\", \"prediction\"]]\n",
    "results.rename(columns={\"prediction\": \"move\"}, inplace=True)\n",
    "actual = combined[[\"week\", \"store\", \"brand\", \"actual\"]]\n",
    "actual.rename(columns={\"actual\": \"move\"}, inplace=True)\n",
    "store_list = combined[\"store\"].unique()\n",
    "brand_list = combined[\"brand\"].unique()\n",
    "\n",
    "plot_predictions_with_history(\n",
    "    results,\n",
    "    actual,\n",
    "    store_list,\n",
    "    brand_list,\n",
    "    \"week\",\n",
    "    \"move\",\n",
    "    grain1_name=\"store\",\n",
    "    grain2_name=\"brand\",\n",
    "    min_timestep=137,\n",
    "    num_samples=6,\n",
    "    predict_at_timestep=135,\n",
    "    line_at_predict_time=False,\n",
    "    title=\"Prediction results for a few sample time series\",\n",
    "    x_label=\"time step\",\n",
    "    y_label=\"target value\",\n",
    "    random_seed=6,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Reading\n",
    "\n",
    "\\[1\\] Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. WaveNet: A Generative Model for Raw Audio. arXiv preprint\n",
    "arXiv:1609.03499 (2016)<br>\n",
    "\n",
    "\\[2\\] Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv preprint arXiv:1803.01271 (2018)<br>"
   ]
  }
 ],
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   "affiliation": "Microsoft",
   "created_by": "Chenhui Hu"
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